Scalability

Overview This comparison covers whether a server or protocol retains session state between requests, or treats every request as a fully self-contained unit with no memory of prior ones. The choice determines how you scale, fail over, and route traffic across instances. Comparison Diagram StatefulStatelessClientServer Asession: id=42must returnto same serverServer Bno session dataClientcarries tokenServerServerany server canhandle the requestStateful: server pins session context and routing depends on itStateless: request carries all context, any node can serve it Comparison Table Aspect Stateful Stateless Request context Server retains prior interaction data across requests Each request carries all context needed to process it Session storage Held in server memory or local session store None on server; state lives in client token or database Routing requirement Requests must reach the same server (sticky sessions) Any server instance can handle any request Scaling model Vertical or sticky-session horizontal scaling only Trivial horizontal scaling, load balance freely Failure recovery Server crash loses in-memory session unless replicated Server crash has no session impact, retry hits any node Client design Client can be thin, server tracks progress Client or token must resend full context each call Typical examples Database connections, WebSocket sessions, FTP REST APIs, HTTP with JWT, DNS lookups Key Differences Stateful servers keep session memory; stateless servers keep none between calls Stateless systems need no sticky routing, simplifying load balancers Stateful failover requires session replication to avoid data loss Stateless designs push state into the client or token instead of the server Horizontal scaling is near-free for stateless architectures When to Use Each Stateful ...

Overview Both are techniques for scaling a database beyond a single node, but they solve different problems: replication copies the entire dataset onto multiple nodes to boost availability and read capacity, while sharding splits the dataset into disjoint partitions across nodes to boost storage and write capacity. Large-scale systems typically use both together — sharding for horizontal scale, replication within each shard for durability. Comparison Diagram Replication Sharding Primary A B C D Replica A A B C D Replica B A B C D full dataset, copied to every node Router key lookup Shard 1 keys A-M Shard 2 keys N-Z dataset split into disjoint subsets Comparison Table Aspect Replication Sharding Primary goal Increase availability and read capacity Increase storage and write capacity Data distribution Full dataset copied to every node Dataset split into disjoint partitions across nodes Write path Writes go to primary, then propagate to replicas Writes routed to the single shard owning the key Read path Any replica (or primary) can serve any read Read must be routed to the shard holding the key Node failure impact Data survives since other copies exist That shard’s data becomes unavailable unless also replicated Consistency concern Replication lag between primary and replicas Cross-shard transactions and joins are hard to coordinate Scaling ceiling Bounded by primary’s write throughput Bounded by cross-shard coordination and key hotspots Operational overhead Failover and leader election Shard key design, rebalancing, and resharding Key Differences Replication duplicates the same data everywhere; sharding partitions it so each node holds only a slice Replication scales reads and durability; sharding scales writes and total storage Sharding introduces a routing layer that must know which shard owns a given key Losing a replica is harmless, but losing an unreplicated shard causes real data loss Production systems commonly combine both: shard for scale, replicate each shard for resilience When to Use Each Replication ...

Overview Vertical scaling grows capacity by adding more CPU/RAM to a single machine, while horizontal scaling grows capacity by adding more nodes behind a load balancer. The choice shapes your application’s architecture, failure model, and cost curve as it grows. Comparison Diagram VerticalHorizontalServerServer+CPU +RAMServer++CPU ++RAMLoad BalancerNodeNodeNode+NodeSingle node, growingMany nodes, distributed Comparison Table Aspect Vertical Scaling Horizontal Scaling Scaling mechanism Add CPU, RAM, or faster disks to one machine Add more machines/nodes to a shared pool Architecture requirement Works with any app, no code changes needed Requires stateless design, load balancing, and shared state (session store, distributed cache) Upper limit Capped by the largest hardware SKU available Effectively unbounded, limited only by orchestration and cost Downtime during scale-up Often requires reboot or migration to bigger instance New nodes join the pool live, no downtime Fault tolerance Single point of failure — one box, one crash Node failures are absorbed by the remaining pool Cost curve Price rises non-linearly at the high end (diminishing returns) Roughly linear cost per added unit of capacity Operational complexity Low — one server to patch, monitor, and secure Higher — needs service discovery, distributed monitoring, data consistency handling Typical use case Monolithic apps, relational databases, legacy systems Stateless web services, microservices, cloud-native workloads Key Differences Vertical scaling upgrades a single machine; horizontal scaling adds more machines to a pool Horizontal scaling demands stateless services, while vertical scaling needs no architectural change Vertical scaling has a hard hardware ceiling; horizontal scaling scales near-linearly A single oversized server is a single point of failure, unlike a distributed node pool Horizontal scaling trades simplicity for operational complexity in orchestration and consistency When to Use Each Vertical Scaling ...

Overview This comparison covers how servers handle client context across requests: a stateless server treats every request as self-contained with no memory of what came before, while a stateful server keeps track of session data tied to a specific client across multiple requests. The choice shapes how easily a system scales, recovers from failure, and routes traffic under load. Comparison Diagram StatelessStatefulClientLoadBalancerServer AServer BServer Cany server can handle any requestno session stored server-sideClientServer+ SessionstickyServerServermust always return tosame server holding session Comparison Table Aspect Stateless Stateful Request self-sufficiency Each request carries all data needed to process it, independent of prior requests Each request depends on context accumulated from prior requests in the same session State storage location Held externally (client token, database, cache) or not persisted at all Held in server memory or local storage tied to a specific server instance Load balancing Any available server can handle any request; simple round-robin routing Requests must be routed to the specific server holding the session (sticky sessions) Horizontal scaling Add or remove server instances freely with no coordination needed Requires state replication or migration before instances can be added or removed Failure recovery A crashed server loses nothing; the next request is simply retried elsewhere A crashed server can drop the active session unless state was replicated Resource footprint per server Lower memory overhead since no per-client data is retained between requests Higher memory/storage overhead from tracking active sessions Typical examples REST APIs, DNS lookups, serverless functions, CDN edge nodes Database connections, WebSocket sessions, FTP, multiplayer game servers Key Differences Stateless servers require no session affinity; stateful servers need sticky routing to reach the same instance. State in stateless systems lives in external stores or the client; state in stateful systems lives in server memory. Stateless architectures scale horizontally with ease, while stateful ones need state replication to scale out. A crashed stateless server loses nothing, but a crashed stateful server can drop an active session. When to Use Each Stateless ...

Overview Horizontal and vertical scaling are the two fundamental strategies for adding capacity to a system: one adds more nodes working in parallel, the other adds more resources to a single existing node. The choice shapes cost, downtime, fault tolerance, and how much your application architecture has to change to support it. Comparison Diagram Horizontal ScalingVertical ScalingLoad BalancerS1S2S3+scale out: add identical nodesno downtime, redundantServer2 CPU / 4GBSame Server16 CPU / 64GBupgradedscale up: add CPU/RAM/diskoften needs downtime Comparison Table Aspect Horizontal Scaling Vertical Scaling Mechanism Add more machines/nodes to the pool Add more CPU, RAM, or disk to an existing machine Implementation Requires a load balancer and clustering to distribute work Swap hardware or resize the VM/instance in place Downtime Typically none; new nodes join the pool live Usually requires a reboot or maintenance window Application requirements App must be stateless or handle distributed state App can remain unaware, since it still runs on one node Cost model Roughly linear cost per added commodity node Cost rises steeply at high-end hardware tiers Fault tolerance Redundant; a node failing doesn’t take the system down Single point of failure; that node failing is an outage Capacity ceiling Practically unbounded, add nodes as needed Bounded by the largest machine/instance available Typical use case Web-scale services, microservices, cloud-native apps Databases, legacy monoliths, short-term quick fixes Key Differences Horizontal scaling adds more nodes in parallel, while vertical scaling adds more resources to one existing node. Horizontal scaling needs a load balancer and app-level statelessness; vertical scaling needs no architectural change. Vertical scaling eventually hits a hardware ceiling; horizontal scaling can grow near-limitlessly. Vertical scaling usually requires downtime to resize, while horizontal scaling can add capacity live. Horizontal scaling improves fault tolerance through redundancy; vertical scaling keeps a single point of failure. When to Use Each Horizontal Scaling ...